In this paper, we extend upon our previous work, Dave et al. [1]} (RIDE-CS), where we used recurrent image density estimator (RIDE) for CS reconstruction. We observed that the sequential nature of recurrent networks in RIDE makes it too slow for inference and training (computational cost is proportional to the image size). Also, in our experiments, the two layer RIDE fails to yield results comparable to recent approaches like OneNet [2]}. Here, we explore sophisticated deep autoregressive models which are order faster than RIDE-CS for both training and inference. We apply the deep autoregressive model based inference to recent frameworks in computational imaging like LiSens [3]} and FlatCam [4]}. We enhance the inference algorithm by incorporating the augmented Lagrangian method when necessary.
In addition, we improve texture recovery using pixel-wise stochastic gradient updates.
